STCM: A spatio-temporal calibration model for low-cost air sensors

Zhang, Yingjun1,2; Ju, Chang1,2; Qin, Jiahu3; Song, Liyan4,5; Liu, Xiaoqian1; Sun, Wei1; Li, Zongxi1

2023-10-01

10.1016/j.ins.2023.119307

INFORMATION SCIENCES   影响因子和分区

0020-0255

1872-6291

Air pollution is one of the most common health-threatening factors, potentially causing millions of deaths every year. Static stations typically collect air pollution data by utilizing a small number of costly and high-quality sensors and numerous low-cost micro stations. However, data collected from micro stations usually suffers from large noise and thus calibration would be necessary for operating good air pollution governance. Point-to-point models and sequence-to-point models have the potential limitations of either failing to mine latent patterns embedded in historical time series or ignoring spatial dependency within a certain region. To address these issues, we propose a novel method called Spatio-Temporal Calibration Model (STCM) based on dual encoders. STCM consists of long-term encoder, short-term encoder, and decoder modules. The long-term encoder encodes historical reference data via GRU and extracts the trend, periodicity, and adjacency of the target pollutant through a temporal attention mechanism. The short-term encoder then reflects real-time conditions through a spatial attention mechanism, quantifying dynamic station -wise correlations between micro stations and the static station. The decoder ultimately integrates outputs of dual encoders and generates calibration results of all micro stations. STCM has been experimentally justified by comparing against nine baseline methods based on two real-world datasets.

Sensor calibration Attention mechanism Deep learning Recurrent neural network Spatio-temporal prediction

SCI

英语

其他

National Key Research and Development Program of China[2022YFB2603302] ; National Nature Science Foundation of China["62002148","51827813"] ; Ramp;D Program of Beijing Municipal Education Commission[KJZD20191000402]

Computer Science

Computer Science, Information Systems

WOS:001023613200001

ELSEVIER SCIENCE INC

COMPUTER SCIENCE

Web of Science

1.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing Key Lab Traff Data Anal & Min, Beijing 100044, Peoples R China
2.Beijing Jiaotong Univ, Key Lab Beijing Railway Engn, Beijing 100044, Peoples R China
3.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China
4.Southern Univ Sci & Technol, Res Inst Trustworthy Autonomous Syst, Shenzhen, Peoples R China
5.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain inspired Intelligent, Shenzhen, Peoples R China

Zhang, Yingjun,Ju, Chang,Qin, Jiahu,et al. STCM: A spatio-temporal calibration model for low-cost air sensors[J]. INFORMATION SCIENCES,2023,644.

Zhang, Yingjun.,Ju, Chang.,Qin, Jiahu.,Song, Liyan.,Liu, Xiaoqian.,...&Li, Zongxi.(2023).STCM: A spatio-temporal calibration model for low-cost air sensors.INFORMATION SCIENCES,644.

Zhang, Yingjun,et al."STCM: A spatio-temporal calibration model for low-cost air sensors".INFORMATION SCIENCES 644(2023).